Modern wireless communication systems transmit digital data (including digitized voice signals) across an air interface by modulating the data onto an RF carrier. The RF signal is received and processed by a receiver, to recover the data. However, the received signal includes, in addition to the data, interference and noise components that must be quantified (or estimated) and removed. A common measure of signal quality is the Signal to Interference plus Noise Ratio, SINR, (or commonly just SIR). The SIR of each radio channel plays an important role in a receiver.
When the SIR is low, signal quality may be too low for correct reception (even with the use of correctional codes) of the actual digital data stream that is sent over the radio channel, and therefore retransmissions may be needed. When the SIR is high, valuable system resources such as time, radio bandwidth and battery power may be wasted, since it may be too easy to correctly receive the actual digital data stream. In either case, the sender may want to adjust the signal power, any controllable sources of interference, and/or the amount (or rate) of useful data that is sent using given time, frequency, and code resources. In order to successfully perform such adjustments, accurate and responsive estimates of the SIR are useful.
Many approaches to SIR estimation focus on estimating input SIR at the input to the demodulator. For example, SIR is often estimated using so-called pilot symbols, which are known data symbols that are transmitted over the air on the actual radio channel. The receiver may compare its received pilot symbols with the known data symbols, interpret the differences as interference plus noise, and calculate an estimated SIR. Such an approach does not account for the fact that some of the interference will be removed by the demodulator, particularly when the demodulator performs channel equalization.
A more useful measure of performance is output SIR, at the output of the demodulator. The better the demodulator is, the higher the SIR estimate.
Much effort has been focused on output SIR estimation for linear demodulation, such as Rake reception or linear equalization, using soft data symbol estimates to estimate SIR. A soft data symbol is where estimated data symbols may be quantized at more levels than there are symbols in the alphabet being used, with the value of the estimated data symbol indicating the degree of confidence that the estimate is correct. For one example of using soft data symbol estimates to estimate SIR, see the paper by K. Higuchi et al., “Experimental evaluation of combined effect of coherent Rake combining and SIR-based fast transmit power control for reverse link of DS-CDMA mobile radio,” published in the IEEE J. Selected Areas Commun., vol. 18, pp. 1526-1535, August 2000, the disclosure of which is incorporated herein by reference in its entirety. Using soft data symbol estimates to estimate SIR involves three steps: modulation removal using detected symbols; estimating a mean then squaring to obtain signal power; and estimating a variance to obtain impairment power.
Recently, such approaches have been extended to operate on soft bit estimates. See co-pending U.S. patent application Ser. No. 12/709,239, titled “Data-aided SIR estimation,” by Rosenqvist, et al., filed concurrently herewith, assigned to the assignee of the present application, and incorporated herein by reference in its entirety. A soft bit value is an integer than indicates both the probable value of a demodulated bit (e.g., zero or one), and additionally an indication of the confidence or probability that the bit has that value. For example, a soft bit may be a positive or negative numerical value, with the sign of the value indicating the digital bit value, and the magnitude of the value indicating the probability that the indicated sign is correct.
Better demodulation performance can be obtained by nonlinear equalization. In particular, Maximum Likelihood Detection (MLD) methods, including Maximum Likelihood Sequence Detection (MLSD), are optimal in the sense that they minimize symbol block or symbol sequence error rate. However, such approaches typically produce hard symbol estimates. Soft bit values are also generated using approaches such as Soft Output Viterbi Algorithm (SOVA), as described by J. Hagenauer and P. Hoeher in the paper, “A Viterbi algorithm with soft-decision outputs and its applications,” published in Proc. IEEE Globecom, Dallas, Tex., 1989, pp. 1680-1686, the disclosure of which is incorporated herein by reference in its entirety.
In the past, SIR estimation for MLSD was based on estimating signal plus impairment power by estimating received signal power, then estimating impairment power using the branch metrics in the MLSD demodulation process. In U.S. Pat. No. 5,909,465, titled, “Method and apparatus for bidirectional demodulation of digitally modulated signals,” by Bottomley, et al., the disclosure of which is incorporated herein by reference in its entirety, the SIR estimate is used to determine a direction of demodulation. In this approach, SIR estimation must be built into the demodulator. Also, because of decision errors, the impairment power estimate is biased low.
Another approach to SIR estimation for MLSD takes advantage of forward error correction (FEC) decoding, as described by K. Balachandran, et al. in the paper, “Channel quality estimation and rate adaptation for cellular mobile radio,” published in the IEEE J. Selected Areas Commun., vol. 17, pp. 1244-1256, July 1999, the disclosure of which is incorporated herein by reference in its entirety. The decoded information bits are re-encoded, re-modulated, and channel filtered to obtain an ideal received signal. This ideal signal is subtracted from the actual received signal to form impairment values, which are squared and averaged to obtain an impairment power. With this approach, regeneration requires successful decoding, some delay, possible extra hardware, and/or more battery power.